Measurement and signature intelligence analysis and reduction technique

ABSTRACT

Methods and apparatus compress data, comprising an In-phase (I) component and a Quadrature (Q) component. Statistical characteristics of the data are utilized to convert the data into a form that requires fewer bits in accordance with the statistical characteristics. The data may be further compressed by transforming the data and by modifying the transformed data in accordance with a quantization conversion table that is associated with the processed data. Additionally, redundancy may be removed from the processed data with an encoder. Subsequent processing of the compressed data may decompress the compressed data in order to approximate the original data by reversing the process for compressing the data with corresponding inverse operations. Interleaved I and Q components can be processed rather than separating the components before processing the data. The processed data type may be determined by providing metadata to retrieve the appropriate quantization table from a knowledge database.

This application is a continuation application of U.S. application Ser.No. 10/776,310 of commonly-owned U.S. Pat. No. 7,136,010 filed on Feb.11, 2004, and issued on Nov. 14, 2006, which is a continuation-in-partof Ser. No. 10/269,818 common-owned, U.S. Pat. No. 6,714,154 filed onOct. 11, 2002, and issued on Mar. 30, 2004, and claiming priority toprovisional U.S. Application No. 60/392,316, filed Jun. 28, 2002, thecontents of each being bodily incorporated by references in itsentirety.

FIELD OF THE INVENTION

The present invention relates to compressing and decompressing data suchas synthetic aperture radar data.

BACKGROUND OF THE INVENTION

Compression of Synthetic Aperture Radar (SAR) data may require that bothmagnitude and phase information be preserved. FIG. 1 shows dataprocessing of synthetic aperture radar data according to prior art.Synthetic aperture radar data 102 are typically collected in analogformat by an antenna 101 and is converted to digital format through anAnalog-to-Digital (A/D) converter 103. The raw, unprocessed data arereferred to as Video Phase History (VPH) data 104, and comprise twocomponents: In-phase (I) and Quadrature (Q). Video phase history data104 having multiple components, such as I and Q, are typically referredas complex SAR data. Complex SAR data are essential for the generationof complex SAR applications products such as interferograms,polarimetry, and coherent change detection, in which a plurality of suchimages must be processed and compared.

Video phase history data 104 are then passed through a Phase HistoryProcessor (PHP) 105 where data 104 are focused in both range(corresponding to a range focusing apparatus 107) and azimuth(corresponding to an azimuth focusing apparatus 109). The output ofphase history processor 105 is referred to as Single Look Complex (SLC)data 110. A detection function 111 processes SLC data 110 to form adetected image 112.

Existing complex SAR sensors collect increasingly large amounts of data.Processing the complex data information and generating resultant imageryproducts may utilize four to eight times the memory storage andbandwidth that is required for the detected data (I&Q). In fact, somestudies suggest exponential growth in associated data throughput overthe next decade. However, sensors are typically associated with on-boardprocessors that have limited processing and storage capabilities.Moreover, collected data are often transmitted to ground stations over aradio channel having a limited frequency bandwidth. Consequently,collected data may require compression in order to store or transmitcollected data within resource capabilities of data collectingapparatus. Also, a SAR compression algorithm should be robust enough tocompress both VPH data 104 and SLC SAR data 110, should produce visuallynear-lossless magnitude image, and should cause minimal degradation inresultant products 112.

Several compression algorithms have been proposed to compress SAR data.However, while such compression algorithms generally work quite well formagnitude imagery, the compression algorithms may not efficientlycompress phase information. Moreover, the phase component may be moreimportant in carrying information about a SAR signal than the magnitudecomponent. With SAR data 102, compression algorithms typically do notachieve compression ratios of more than ten to one without significantdegradation of the phase information. Because many of the compressionalgorithms are typically designed for Electro/Optical (EO) imagery, thecompression algorithms rely on high local data correlation to achievegood compression results and typically discard phase data prior tocompression. Table 1 lists several compression algorithms discussed inthe literature and provide a brief description of each.

TABLE 1 Popular Alternative SAR Data Compression Algorithms CompressionAlgorithm Description Block Adaptive Quantization Choice of onboard datacompression (BAQ) methods due to simplicity in coding and decodinghardware. Low compression ratios achieved (<4:1). Vector Quantization(VQ) Codebook created assigning a number for a sequence of pixels.Awkward implementation since considerable complexity required incodebook formulation. Block Adaptive Vector Consists of firstcompressing data with Quantization (BAVQ) BAQ and then following up withVQ. Similar to BAQ. Karhunen-Loeve Transform Statistically optimaltransform for (KLT) providing uncorrelated coefficients; however,computational cost is large. Fast Fourier Transform 2-D Fast FourierTransform (FFT) BAQ (FFT-BAQ) performed on raw SAR data. Before raw datais transformed, dynamic range for each block is decreased using a BAQ.Uniform Sampled Quantization Emphasizes phase accuracy of selected (USQ)points. Flexible BAQ (FBAQ) Based on minimizing mean square errorbetween original and reconstructed data. Trellis-Coded QuantizationUnique quantizer optimization design. (TCQ) Techniques provide superiorsignal to noise ratio (SNR) performance to BAQ and VQ for SAR. BlockAdaptive Scalar BSAQ's adaptive technique provides Quantization (BSAQ)some performance improvement.

Existing optical algorithms are inadequate for compressing complexmulti-dimensional data, such as SAR data compression. For example withoptical imagery, because of a human eyesight's natural high frequencyroll-off, the high frequencies play a less important role than lowfrequencies. Also, optical imagery has high local correlation and themagnitude component is typically more important than the phasecomponent. However, such characteristics may not be applicable tocomplex multi-dimensional data. Consequently, a method and apparatusthat provides a large degree of compression without a significantdegradation of the processed signal are beneficial in advancing the artin storing and transmitting complex multi-dimensional data. Furthermore,the quality of the processed complex multi-dimensional data is nottypically visually assessable. Thus, a means for evaluating the effectsof compression on the resulting processed signal is beneficial toadjusting and to evaluating the compression process.

BRIEF SUMMARY OF THE INVENTION

The present invention provides methods and apparatus for compressingdata comprising an In-phase (I) component and a Quadrature (Q)component. The compressed data may be saved into a memory or may betransmitted to a remote location for subsequent processing or storage.Statistical characteristics of the data are utilized to convert the datainto a form that requires a reduced number of bits in accordance withits statistical characteristics. The data may be further compressed bytransforming the data, as with a discrete cosine transform, and bymodifying the transformed data in accordance with a quantizationconversion table that is selected using a data type associated with thedata. Additionally, a degree of redundancy may be removed from theprocessed data with an encoder. Subsequent processing of the compresseddata may decompress the compressed data in order to approximate theoriginal data by reversing the process for compressing the data withcorresponding inverse operations.

In a first embodiment of the invention, data are compressed with anapparatus comprising a preprocessor, a transform module, a quantizer, anencoder, and a post-processor. The preprocessor separates the data intoan I component and a Q component and bins each component according tostatistical characteristics of the data. The transform module transformsthe processed data into a discrete cosine transform that is quantized bythe quantizer using a selected quantization conversion table. Theencoder partially removes redundancy from the output of the quantizerusing Huffman coding. The resulting data can be formatted by apost-processor for storage or transmittal. With a second embodiment, thepreprocessor converts the I and Q components into amplitude and phasecomponents and forms converted I and Q components.

Variations of the embodiment may use a subset of the apparatus modulesof the first or the second embodiment. In a variation of the embodiment,the apparatus comprises a preprocessor, a transform module, and aquantizer.

With another embodiment of the invention, interleaved I and Q componentsof SAR data can be processed rather than separating the componentsbefore processing the data. Thus, one is not constrained to separatelycompress and decompress the I and Q data. With the processing ofinterleaved I and Q data, one may assume that the statisticalcharacteristics are the same, in which a deviation from this assumptionmay result in a reduced compression ratio. Moreover, the processing ofinterleaved data is applicable to data that are characterized by one ormore dimensions.

With another embodiment of the invention, the data type being processedfor compression and decompression is determined. With the compressionprocess, the preprocessor interacts with the quantizer. The preprocessorprovides metadata to the quantizer so that the quantizer can retrievethe appropriate quantization table from a knowledge database. Themetadata may include statistical characteristics of the data beingprocessed such as the mean, the standard deviation, and themaximum/minimum values. This aspect of the invention also supports atraining mode, in which the quantizer informs the preprocessor so that anew data type can be specified.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features and wherein:

FIG. 1 shows data processing of synthetic aperture radar data accordingto prior art;

FIG. 2 shows an apparatus for compressing data in accordance with anembodiment of the invention;

FIG. 3 shows a preprocessor apparatus for preprocessing a complex imagein accordance with an embodiment of the invention;

FIG. 4A shows a process for binning data associated with a complex imagein accordance with an embodiment of the invention;

FIG. 4B shows a process for truncating magnitude and phase components ofa complex image in accordance with an embodiment of the invention;

FIG. 5 shows probability density functions that are associated withIn-phase (I) and Quadrature (Q) components of exemplary syntheticaperture radar (SAR) data;

FIG. 6 shows Root Mean Square Error (RMSE) values that are associatedwith magnitude and phase data for processed signal data as shown in FIG.2 in accordance with an embodiment of the invention;

FIG. 7 shows a partitioning of complex image data in order to obtainDiscrete Cosine Transform (DCT) in accordance with an embodiment of theinvention;

FIG. 8 shows an apparatus for quantizing Discrete Cosine Transform (DCT)data in accordance with an embodiment of the invention;

FIG. 9 shows a representative histogram for a low order Discrete CosineTransform (DCT) coefficient in accordance with an embodiment of theinvention;

FIG. 10 shows a representative histogram for a high order DiscreteCosine Transform (DCT) coefficient in accordance with an embodiment ofthe invention;

FIG. 11 shows a heuristic process for determining a quantization matrixaccording to an embodiment of the invention;

FIG. 12 shows an apparatus for decompressing data in accordance with anembodiment of the invention;

FIG. 13 shows an architecture for processing synthetic aperture radardata in which components are split in accordance with an embodiment ofthe invention;

FIG. 14 shows a data stream comprising interleaved components inaccordance with an embodiment of the invention;

FIG. 15 shows splitting of interleaved components into constituentcomponents in accordance with an embodiment of the invention;

FIG. 16 shows an architecture for processing synthetic aperture data inwhich components are interleaved in accordance with an embodiment of theinvention;

FIG. 17 shows a second apparatus for quantizing Discrete CosineTransform (DCT) data in accordance with an embodiment of the invention;and

FIG. 18 shows a flow diagram for processing data header information todetermine a data type in accordance with an embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION (FIRST-GENERATION EMBODIMENTS)

In the following description of the various embodiments, reference ismade to the accompanying drawings which form a part hereof, and in whichis shown by way of illustration various embodiments in which theinvention may be practiced. It is to be understood that otherembodiments may be utilized and structural and functional modificationsmay be made without departing from the scope of the present invention.

FIG. 2 shows an apparatus 200 for compressing Synthetic Aperture Radar(SAR) data 202 in accordance with an embodiment of the invention.Synthetic Aperture Radar (SAR) data 202 can be compressed by apparatus200 from Video Phase History (VPH) data format 104 or from a processedversion typically referred as a single look complex SLC format 110.There are advantages and disadvantages associated with each format. VPHdata 104 is available almost immediately, but is highly uncorrelated.Single look complex SLC data 110 exhibits some local correlation. SLCdata 110 may yield slightly better compression results than with VPHdata 104, but SLC 110 data are only available after processing hasoccurred.

Other embodiments of the invention may support other applications ofcomplex multidimensional data, including weather data, oil and gasexploration data, encrypted/decrypted data, medical archival of MRI/CTIand three dimensional sonograms, digital video signals, and modemapplications.

Referring again to FIG. 2, apparatus 200 comprises a preprocessor 201, atransform module 203, a quantizer 205, an encoder 207, and apost-processor 209 in order to provide compressed data 212. SAR data 202may comprise SAR pixel data that may be provided in the form of twofloating-point numbers representing In-phase (I) and Quadrature (Q)components. (SAR data 202 may be considered as being “received” eventhough the data may not be received from a radio receiver but obtainedfrom a memory that stores the data.) Preprocessor 201 may convert the Iand Q components to Magnitude (M) and Phase (φ) components in accordancewith a second embodiment as will be discussed in the context of FIG. 4B.Additionally, preprocessor 201 may convert the I and Q components intomagnitude and phase components to facilitate viewing SAR data 202. Themagnitude and the phase components may be obtained from the in-phase andquadrature components by using Equations 1 and 2.M=(I ² +Q ²)^(1/2)  (EQ. 1)φ=tan⁻¹(Q/I)  (EQ. 2)Moreover, I and Q components may be obtained from the magnitude and thephase components by using Equations 3 and 4.I=M cos φ  (EQ. 3)Q=M sin φ  (EQ. 4)Additionally, the power of a SAR signal may provide good visual resultswhen printing intensity (magnitude-only) imagery. The power of a SARsignal may be obtained from Equation 5.P=20 log₁₀M²  (EQ. 5)

The conversion between (I, Q) and (M, φ) as expressed in EQs. 1-4 allowsSAR data 202 to be studied in both data formats before and aftercompression. When SAR data 202 are represented as magnitude and phasecomponents, additional bits may be allocated to the phase componentversus the magnitude component to achieve the least degradation of thephase product, depending on characteristics of SAR data 202. In anembodiment, more bits (e.g. six bits) of the phase component and fewerbits (e.g. two bits) of the magnitude component are used to generatecompressed I and Q components. Conversely, when SAR data 202 arerepresented by in-phase and quadrature components, apparatus 200 canprocess the in-phase component separately from the quadrature componentfor a single complex image.

Preprocessor 201 also determines a data type (as discussed in thecontext of FIG. 3) and informs quantizer 205 through an adaptive controlloop 251.

FIG. 3 shows preprocessor apparatus 201 (as shown in FIG. 2) forpreprocessing complex image 202 in accordance with an embodiment of theinvention. Preprocessor apparatus 201 reduces the number of bits thatare needed to represent complex data (I,Q) within a specifieddegradation (corresponding to an error metric). Typically, VPH data 104or SLC 110 data are represented by (I,Q) data pairs 202, in which eachpair uses 64, 32, or 16 bits, and where I and Q are separatelyrepresented in 32, 16, or 8-bit formats, respectively. Data 202 may beformatted in which an ordering of the most significant to the leastsignificant bytes may be reversed with respect to the assumed order thatpreprocessor 201 processes data 202. In such a case, preprocessor 201may perform “byte swapping” to reorder data 202 in accordance with theassumed ordering of the constituent bytes.

An adaptive source data calculations module 301 separately processes theI and Q components of (I,Q) data pairs 202 in order to determinecorresponding statistical characteristics. (An example of statisticalcharacteristics is shown in FIG. 5, in which the I component hasapproximately the same statistical characteristics as the Q component.)In the embodiment, a general-purpose computer (e.g. an associatedmicroprocessor) measures the number of occurrences of the I component orthe Q component as a function of the value of the I component or the Qcomponent. Additionally, adaptive source data calculations module 301performs header analysis by reading information provided at thebeginning of a data file comprising data 202 in order to determine theformat of the data being analyzed, e.g. the number of bits that areassociated with (I,Q) data 202. Module 301 also performs data analysisthat provides statistical characteristics of data 202 as may becharacterized by probability density functions of the I component andthe Q component (as exemplified by FIG. 5). Module 301 determines a binassignment that may vary with the value of the I or Q component. In theembodiment, a size of a bin is inversely related to a value of theprobability density function at a midpoint of the bin. A calculationsmodule 303 uses the statistical characteristics of data 202 to assignthe I and Q components into bins. A module 305 uses the bin identity toform the I′ and Q′ components (converted I component and converted Qcomponent, corresponding to data 204 in FIG. 2), having 8-bit integervalues between 0 and 255 by efficiently allocating bins, in which mostof the bins are assigned to a range containing the most data points. Forexample, data (corresponding to either I or Q) may range from −10,000 to+10,000 units, in which over 99.9% of the data are contained with arange of −2,000 to +2,000 units. In such a case, most of the bins wouldbe allocated between the smaller range (i.e. −2000 to +2,000 units)rather than the larger range (i.e. −10,000 to +10,000 units).

In a variation of the embodiment, Single Look Complex (SLC) data 110 aretransformed using a Fast Fourier Transform (FFT) prior to binning data202 by modules 303 and 305, wherein a transformation of SLC data 110 hasstatistical characteristics that are similar to VPH data 104. (In theembodiment, modules 303 and 305 bin data 202 by first processing the Icomponent and subsequently processing the Q component.) However, otherembodiments of the invention may utilize other transform types in orderto modify statistical characteristics of the data. After quantization bymodules 303 and 305, the transformed SLC data are inversely transformedusing an Inverse Fast Fourier Transform (IFFT).

FIG. 4A shows a process for binning data associated with complex imagedata 202, as performed by module 303 in accordance with an embodiment ofthe invention. Complex image data 202 are separated into I and Qcomponents by a module 403. If the most to the least significant bytesneed to be reordered, modules 405 and 413 swap bytes for the I componentand Q component, respectively. Modules 407 and 415 determine theprobability density functions for the I component and the Q component,respectively over data files (comprising static images of a datagathering session). As discussed in the context of FIG. 5, theprobability density functions of the I component and the Q component maybe essentially the same so that embodiments of the invention may utilizeone module by separately processing the I and Q components. Modules 409and 417 bin the I component and the Q component, respectively. Thegreater the probability density function p(x_(i)), where x_(i) is thecenter value of the i^(th) bin, the smaller the range of the i^(th) binin order to provide better resolution for data within the i^(th) bin.

FIG. 4B shows a process for truncating magnitude and phase components ofa complex image in accordance with a second embodiment of the invention.In a second embodiment of the invention, module 305 of preprocessorapparatus 201 may utilize a different number of bits that are associatedwith the phase component (φ) than is associated with the magnitudecomponent (M). In the embodiment, fewer bits from the magnitudecomponent (a truncation of M) and more bits from the phase component (atruncation of φ), as determined from Equations 1 and 2 by converting Iand Q into M and φ, are used to generate compressed components I′ andQ′, as determined from Equations 3 and 4 by converting the truncationsof M and φ into I′ and Q′. Allocating more bits from the phase componenthelps preserve phase information, as may be the case with Video PhaseHistory (VPH) data 104. As shown in FIG. 4B, complex image data 202 isseparated into I and Q components by module 453. The I and Q componentsare converted into magnitude and phase components by module 455. Module457 truncates the magnitude and phase components in order to retain adesired number of bits from each of the components. Module 459 convertsthe truncated portions of the magnitude and phase components to formcompressed components I′ and Q′ (corresponding to data 461).

Apparatus 200 may use the same statistical modeling for the In-phase (I)and Quadrature (Q) components if both components have approximately thesame statistical characteristics. FIG. 5 shows probability densityfunctions that are associated with in-phase and quadrature components ofexemplary synthetic aperture radar data. A number of pixels 501 is shownin relation to a corresponding pixel values 503 for a typical SAR image.A Probability Density Function (PDF) 507 for the in-phase component anda probability density function 505 for the quadrature component areapproximately the same. FIG. 5 suggests that apparatus 200 may processboth the in-phase component and the quadrature components in the sameway without incurring a large error. If probability density function 507is essentially the same as probability density function 505, then onemay obtain a probability density of one of the components (either PDF507 or PDF 505) and approximate the probability density function of theother component by the obtained probability density function. However,other embodiments of the invention may use different statisticalrelationships for the in-phase component and the quadrature component ifthe statistics characteristics differ appreciably.

Preprocessor 201 accommodates different sensor types regarding a dataformat and a number of bits per pixel. (A pixel corresponds to a pointin the corresponding image being scanned by a radar system.) SAR data202 are typically 64 bits (with 32 bits for the I component and 32 bitsfor the Q component for each pixel) or 32 bits (with 16 bits for the Icomponent and 16 bits for the Q component for each pixel). Preprocessor201 determines the range of pixel values and the best bin assignment.Values of the I and Q components are converted to 8-bit formats withmore bits being allocated from the associated phase component than themagnitude component before reducing the I and Q components to 8-bitformats. (As previously discussed, two bits from the magnitude componentand six bits from the phase component, as determined from Equations 1and 2 by converting I and Q into M and φ, are used to generatecompressed components I′ and Q′, as determined from Equations 3 and 4 byconverting the truncations of M and φ into I′ and Q′.)

FIG. 6 shows Root Mean Square Error (RMSE) values that are associatedwith magnitude and phase data for processed data (e.g. processed SARdata 204) as shown in FIG. 2 in accordance with an embodiment of theinvention. (The root mean square error is a measure of the quantizationerror by relating the compressed data with the original data.) Values601 are related to an assigned number of bits per pixel 603 for phasedata 605, magnitude data 607 (with a linear-log representation), andmagnitude data 609 (no linear-log representation). Similarly,calculations may be performed for (I, Q) data. Root mean square errorand Peak Signal to Noise Ratio (PSNR) figures of merit may be initiallyused as a basis for designing preprocessor 201 and for the evaluatingthe compressed imagery.

Processed SAR data 204 (comprising a converted I component and aconverted Q component) are further processed through transform module203 using a Discrete Cosine Transform (DCT) in order to obtain thefrequency representation of the in-phase and the quadrature data astransformed data 206 (comprising a transformed I component and atransformed Q component). As will be discussed in the context of FIG. 7,the converted I component and the converted Q component of SAR data 204are separately partitioned into smaller blocks. (Each block isessentially independent of other blocks so that each block may beprocessed individually in order to process an entire image.) Thediscrete cosine transform is well known in the art, and is given byEquation 6.

$\begin{matrix}{{B\left( {k_{1},k_{2}} \right)} = {\sum\limits_{i = 0}^{N_{1} - 1}\;{\sum\limits_{j = 0}^{N_{2} - 1}\;{4 \cdot {A\left( {i,j} \right)} \cdot {\cos\left\lbrack {\frac{\pi \cdot k_{1}}{2 \cdot N_{1}} \cdot \left( {{2 \cdot i} + 1} \right)} \right\rbrack} \cdot {\cos\left\lbrack {\frac{\pi \cdot k_{2}}{2 \cdot N_{2}} \cdot \left( {{2 \cdot j} + 1} \right)} \right\rbrack}}}}} & \left( {{EQ}.\mspace{14mu} 6} \right)\end{matrix}$In Equation 6, pair (i,j) represents a pixel of processed SAR data 204within a block (which is a portion, A(i,j) represents a correspondingin-phase or quadrature value of the pixel, and B(k₁,k₂) represents acorresponding DCT coefficient, where pair (k₁,k₂) identifies the DCTcoefficient in the DCT matrix. In the embodiment, a DCT coefficient iscalculated over an eight by eight pixel block, i.e. N₁ and N₂ equal 8,although other embodiments of the invention may use a different valuefor N. (The collection of DCT coefficients may be represented by an 8 by8 matrix.)

FIG. 7 shows a partitioning of complex image data in order to obtainDiscrete Cosine Transform (DCT) data in accordance with an embodiment ofthe invention. In the embodiment, SAR data 202 comprise the I componentand the Q component, each component corresponding to a large (such as1024 by 1024) data file 701. Transform module 203 partitions each file701 into a square (such as a 8 by 8 block for the DCT matrix), e.g.blocks 703 and 705. Transform module 203 processes each block (e.g. 703and 705) in accordance with Equation 6. In order to process the entiredata file 701, preprocessor 201 processes 128 partitions for both the Icomponent and the Q component.

FIG. 8 shows apparatus 205 for quantizing Discrete Cosine Transform(DCT) data in accordance with an embodiment of the invention. Quantizer205 comprises an adaptive table generation module 801, an adaptive tableselection module 803, and a perform_data_quantization module 805.Adaptive table generation module 801 generates a new quantizationconversion table (which contains a quantization matrix that is used forfurther data compression as will be explained) for a new data type andstores the new quantization conversion table into a knowledge database807 through an interface 809 when functioning in a training mode but notduring an operational mode. During the operational mode, transformeddata 206 are processed by adaptive table selection module 803 andperform_data_quantization module 805. Depending upon the data type, asidentified by adaptive control loop 251 from preprocessor 201, adaptivetable selection module 803 selects an appropriate quantizationconversion table, which comprises an 8 by 8 quantization matrix, fromknowledge database 807 through an interface 811. If adaptive tableselection module 803 cannot identify an appropriate quantizationconversion table from adaptive control loop 251, module 803 selects adefault quantization conversion table. A quantization conversion tablemay correspond to different data types that are dependent upon factorsincluding the type of radar, processing platform (which may affect thenumber of bits associated with SAR data 202), and topography that isassociated with SAR data 202.

Each element of a DCT matrix (e.g. matrix 703) is arithmetically dividedby a corresponding element of the quantization matrix and rounded to aninteger, thus providing quantized DCT data 208 (comprising a quantized Itransform or a quantized Q transform). Each element of the quantizationmatrix is determined by statistics for the corresponding DCT coefficientin accordance with a specified maximum error (e.g. a root mean squareerror, a peak signal to noise ratio, and a byte by byte filecomparison). (FIGS. 9 and 10 show statistics for the (1,1) and the (7,7)DCT coefficients, respectively.) The larger the value of an element ofthe quantization matrix, the greater the corresponding step size (withless resolution). However, dividing an element of the DCT matrix by alarger number reduces the quantized value. If the quantized value issufficiently reduced, the resulting value may be considered as beingzero by encoder 207 if a specified maximum error (e.g. the root meansquare error) is satisfied.

In a variation of the embodiment, the quantization matrix may bedetermined by reducing a Measurement and Signature Intelligence (MASINT)product distortion. (In some cases, the reduction may correspond to aminimization of the distortion.) The distortion may be determined frominterferometric SAR, coherent change detection (CCD), and polarimetryproducts. Interferometric SAR (IFSAR) is a comparison of two or morecoherent SAR images collected at slightly different geometries. Theprocess extracts phase differences caused by changes in elevation withinthe scene. IFSAR produces digital terrain elevation data suitable foruse in providing terrain visualization products. (Products are generallyreferred as Digital Elevation Models (DEM).) These products are used inmapping and terrain visualization products. The advantage of IFSARheight determination is that is much more accurate than other methods,such as photo/radargrammetry methods that use only the intensity(magnitude) data, because phase is used and height determination is donewith wavelength measurements which are very accurate (i.e. forcommercial systems at C Band (5 GHz) approximately 5.3 cm)).

Coherent Change Detection (CCD) is a technique involving the collectionand comparison of a registered pair of coherent SAR images fromapproximately the same geometry collected at two different times (beforeand after an event). The phase information, not the magnitude, is usedto determine what has changed between the first and second collection.This can determine scene changes to the order of a wavelength (5.3 cm)and may denote ground changes/activity occurring between collections.

Polarimetry products are generally collected using systems that canindependently radiate and collect vertical and horizontal complex SARdata. This technique is accomplished by alternately radiating verticaland horizontally polarized SAR pulses, receiving on both horizontal andvertical antennas, and saving the complex data from each. The productformed is a unique target signature for objects with an associatedcomplex polarized radar reflectance. This technique is used in manyautomatic target recognition systems (ATR).

In a variation of the embodiment, each member of the quantization matrix(associated with a quantization conversion table) is determined by aheuristic process 1100 as shown in FIG. 11. A quantization matrix forSAR data 202 may be determined by selecting an element of thequantization matrix in step 1103 and perturbing the value of theselected element in step 1105. In step 1105, the selected element isincremented and decremented by incremental values. In step 1107, rootmeans square errors (RSME) are calculated for different compressionratios. The selected value of the selected element is the valuecorresponding to a minimal root mean square error. If there are moreelements in the quantization matrix to be processed, as determined instep 1109, the element indices (i,j) are incremented in step 1111, andthe next element is selected in step 1103. Steps 1105 and 1107 arerepeated for the next element. The calculation of the quantizationmatrix is completed after all the elements are processed.

In another variation of the embodiment, the quantization matrix isdetermined by the statistical characteristics of the DCT matrix, as waspreviously discussed. The quantization matrix is subsequently modifiedaccording to heuristic process 1100.

Transformed data 206 are quantized according to corresponding transformstatistics that are associated with the DCT coefficients. DCTcoefficients can be represented as departures from a standardstatistical distribution function (e.g., Laplacian, Gaussian, orRayleigh). (A Laplacian function has a form of e^(−|x|), while aGaussian function has a form of e^(−x) ² .) FIG. 9 shows arepresentative histogram for a low order Discrete Cosine Transform (DCT)coefficient, DCT coefficient (1,1), in accordance with an embodiment ofthe invention. A number of observations 901 is shown in relation tocorresponding bin values 903. Actual data 905 is shown along with aLaplacian relation 907 and a Gaussian relation 909. Also, FIG. 10 showsa representative histogram for a high order Discrete Cosine Transform(DCT) coefficient, DCT coefficient (7,7), in accordance with anembodiment of the invention. A number of observations 1001 is shown inrelation to corresponding bin values 1003. Actual data 1005 is shownalong with a Laplacian relation 1007 and a Gaussian relation 1009.Analysis of the exemplary SAR data reveals a relationship with respectto the low order and high order DCT coefficients. By plotting theLaplacian and Gaussian functions and comparing the corresponding valueswith the DCT coefficient data of the exemplary SAR data, it isdetermined that low order terms can be better represented by theLaplacian function, and the higher order terms can be better representedby the Gaussian function for typical SAR data. Quantization by quantizer205 is designed by accounting for the complex SAR image DCT statisticsas exemplified by FIGS. 9 and 10. As the probability distributionbecomes more focused about a zero value for a DCT coefficient, the lessis the relative significance of the DCT coefficient with respect toother DCT coefficients. Consequently, the corresponding entry in thequantization conversion table may be greater for the DCT coefficient.

Other embodiments of the invention may utilize other transform typessuch as a Discrete Fourier Transform (DFT) or a discrete z-transform,both transforms being well known in the art. However, with a selectionof a different transform, the transform statistics may be different asreflected by the design of quantizer 205.

Quantized SAR data 208 are consequently processed by encoder 207 (e.g. aHuffman encoder). Each output 210 (comprising a compressed I componentand a compressed Q component) of encoder 207 comprises an encoder pair(comprising a number of zeros that precede output 210 and a number ofbits that represent a value of the corresponding DCT coefficient) andthe value of the corresponding DCT coefficient (SAR data 208). Encoder207 may provide additional compression by removing a degree ofredundancy that is associated with the encoder pair and SAR data 208 (inwhich frequently occurring data strings that are associated with thequantized DCT coefficients are replaced with shorter codes). Otherembodiments of the invention may utilize other types of encoders such asShannon Fano coding and Arithmetic coding. Encoder 207 provides encodeddata 210 to post-processor 209.

Post-processor 209 may further process encoded data 210 in order toformat data 210 into a format that is required for storing (that may beassociated with archiving compressed data) or for transmittingcompressed data 212 through a communications device. In the embodiment,the communications device may be a radio frequency transmitter thattransmits from a plane to a monitoring station, utilizing a radio dataprotocol as is known in the art. In the embodiment, for example,post-processor 209 may format a data file (corresponding to a SAR image)into records that can be accommodated by a storage device. Also,post-processor may include statistical information and the data typeregarding SAR data 202. The statistical information and the data typemay be used for decompressing compressed SAR data 212 at a subsequenttime.

Compressed data 212 may be subsequently decompressed by using apparatusthat utilizes inverse operations corresponding to the operations thatare provided by apparatus 200 in a reverse order. FIG. 12 shows anapparatus 1200 for decompressing compressed data 1212 (that wascompressed by apparatus 200 as shown in FIG. 2) into a decompressed data1202 in accordance with an embodiment of the invention. (Decompresseddata 1202 approximates data 202 within a specified maximum error.) Aninverse post-processor 1209, a decoder 1107, an inverse quantizer 1205,an inverse transform module 1203, and an inverse preprocessor 1201correspond to post-processor 209, encoder 207, quantizer 205, transformmodule 203, and preprocessor 201, respectively. However, an inverseoperation may not be able to exactly recover data because ofquantization restraints. For example, quantizer 203 divides a DCTcoefficient by a corresponding element in the quantization matrix (whichis obtained from a quantization conversion table selected by module 803from knowledge database 807) and rounded to an integer. The operation ofrounding to an integer may cause information about the DCT coefficientto be lost. Consequently, the lost information cannot be recovered byinverse quantizer 1205 in determining the DCT coefficient.

In the embodiment, compressed data 212 may be compliant with NationalImagery Transmission Format (NITF) standards, in which headerinformation about user-defined data (e.g. a quantization matrix) may beincluded. Thus, compressed data 212 may be compatible with processingsoftware in accordance with Joint Photographic Experts Group (JPEG)compression standards.

Other embodiments of the invention may compress and decompress data thatare characterized by a different number of components (often referred asdimensions). Data that is characterized by more than one component (e.g.2, 3, or more components) are often referred as multidimensional data.In such cases, preprocessor 201 may determine statisticalcharacteristics associated with each of the components and map each ofthe components to bins in accordance with the statisticalcharacteristics. Transform module 203 transforms each of the componentsaccording to a selected transform (e.g. a Fast Fourier Transform).Quantizer 205 subsequently quantizes each of the transformed components.

Classical Electro-Optical (EO) based metrics, such as root mean squareerror (RMSE) and Peak Signal to Noise Ratio (PSNR), are useful forevaluating the magnitude imagery, but the EO-based metrics may notprovide sufficient information about the phase data or the other derivedproducts. EO-based metrics provide a necessary but not a sufficientcondition for complex data compression fidelity. Useful magnitudeimagery may also be available from the compression process. Theprocesses that generate phase data driven products such asinterferometry, CCD and polarimetry may be included in the evaluations.Additional SAR data product metrics may be implemented to evaluate thephase information and any degradation of the products caused bycompression.

An evaluation of compressed exemplary SAR data as processed by apparatus200 indicates that, with SAR data 202 being compressed at ratios greaterthan twenty to one, apparatus 200 may achieve near-lossless results formagnitude images and minimal degradation to phase information.

DETAILED DESCRIPTION OF THE INVENTION (SECOND-GENERATION EMBODIMENTS)

Processing Interleaved Components

FIG. 13 shows an architecture 1300 for processing synthetic apertureradar data 202 in which components are split in accordance with anembodiment of the invention. Data 202 comprises a received I componentand a received Q component that are interleaved. FIG. 14 shows a datastream 1400 that transports complex SAR data 202. Data stream 1400interleaves received I component samples (samples 1401, 1405, 1409, and1413) with corresponding received Q component samples (samples 1403,1407, 1411, and 1415).

Referring to FIG. 3, as previously discussed, adaptive source datacalculations module 301 splits (separates) the received I component fromthe received Q component before further processing SAR data 202. Witharchitecture 1300, the splitting of I and Q components is functionallyassociated with module 403 (as shown in FIG. 4A). As shown inarchitecture 1300, the I component and the Q component are separatelyprocessed for compressing and decompressing SAR data 202.

FIG. 15 shows splitting data stream 1400 into I component 1551 and Qcomponent 1553. I component 1551 comprises I component samples 1501,1503, 1505, and 1507, and Q component 1553 comprises Q component samples1509, 1511, 1513, and 1515.

Referring to FIG. 13, modules 1301 and 1303 process (compress anddecompress) the I component, and modules 1305 and 1307 process the Qcomponent. In the embodiment, I component data and Q component data aretransferred by modules 1302 and 1306, respectively. (As examples, datamay be transferred through file transfers, data tape transfers or radiotransmission transfers.) The processed data is rendered by module 1309.Architecture 1300 is incorporated into the apparatus as previouslydiscussed in FIG. 3.

FIG. 16 illustrates a second architecture 1600 for processing syntheticaperture data 202 in which components are interleaved in accordance withan embodiment of the invention. With architecture 1600, interleaved Iand Q component samples (for example as shown in FIG. 14) are processedby compress interleaved data module 1601 and decompress interleaved datamodule 1603. In the embodiment, interleaved data are transferred bymodule 1602. (As examples, interleaved data may be transferred throughfile transfers, data tape transfers or radio transmission transfers.)After processing by modules 1601 and 1603, I data 1607 is split from Qdata 1609 by module 1605. Processed data (data 1607 and data 1609) isrendered by module 1611.

In the embodiment, modules 1601 and 1603 processes interleaved data,where the I component and the Q component have approximately the samestatistical characteristics. Typically, the resulting compression ratiodecreases the more that the statistical characteristics of the Icomponent differ from those of the Q component.

Determination of Data Type

FIG. 17 shows an apparatus 1700 that is a variation of apparatus 800 asshown in FIG. 8. As shown in FIG. 17, preprocessor 1701 corresponds topreprocessor 801, transform module 1703 corresponds to module 203,modified quantizer module 1705 corresponds to quantizer 205, encodermodule 1707 corresponds to encoder 207, knowledge base 1711 correspondsto knowledge base 807, interface 1713 corresponds to interface 811, andadaptive control loop 1751 corresponds to adaptive control loop 251 inrelation to FIG. 8. Apparatus 1700 also includes training mode feedback1753 and metadata output 1755, which will be discussed.

As with adaptive control loop 251 as shown in FIG. 8, preprocessor 201(as shown in FIG. 17) provides identification information about the datatype for SAR data 202 to modified quantizer module 1705 through adaptivecontrol loop 1751. In the embodiment, preprocessor 201 providesmetadata, e.g., mean, standard deviation, maximum data value, andminimum data value, from which modified quantizer module 1705 canidentify the data type and retrieve the appropriate quantizationconversion table from knowledge base 1711.

If module 1705 cannot identify the data type from the metadata, module1705 selects a default quantization table and uses the defaultquantization table from knowledge base 1711 as similarly explained inthe context of FIG. 8. Moreover, modified quantizer module 1705 maynotify preprocessor 1701, through training mode feedback loop 1753, thata data type cannot be identified using the provided metadata.Preprocessor 1701 may consequently specify a new quantization tablecorresponding to the metadata.

After quantizing the data, encoder module 1707 encodes the data, aspreviously explained with FIG. 8, and passes the processed data tomodule 1709. Additionally, preprocessor 1701 provides the metadata tomodule 1709 (through metadata output 1755) so that the processed datacan be decompressed in accordance with the corresponding data type.

FIG. 18 shows a flow diagram 1800 for determining the data type from SARdata 202 in accordance with an embodiment of the invention. In theembodiment, flow diagram 1800 is implemented with preprocessor 1701 andmodified quantizer module 1705, although alternative embodiments mayimplement flow diagram 1800 entirely within module 1701 or module 1705or another module or may implement flow diagram 1800 by distributing thefunctionality across a plurality of modules.

Step 1801 performs header analysis if header information is includedwith SAR data 202 by reading information at the beginning of a data filethat identifies the format of the data being analyzed. (The operation ofstep 1801 is similar to the header analysis performed by adaptive sourcedata calculations module 301 previously explained with FIG. 3.) Step1803 parses the header information and the data type is identified instep 1821.

If header data is not available, SAR data 202 is analyzed in order todeduce the metadata. In step 1805, the number of bits per data word isdetermined. If the order of bytes is reversed with respect to theassumed order that preprocessor 201 uses for processing SAR data 202, asdetermined by step 1807, the bytes are swapped in step 1809. (Operationof step 1807 and 1809 are similar to the operation as performed bypreprocessor 201 as shown in FIG. 3.)

In step 1811, the maximum value and the minimum value of SAR data 202are determined. Furthermore, the mean and the standard deviation of SARdata 202 are calculated in step 1813. The metadata (comprising theminimum value, the maximum data, the mean, and the standard deviation)are compared to corresponding metadata of known data types in step 1815.In step 1817, if the data type is known, the data type is identified instep 1821. Otherwise, the data type is assumed to be the default datatype in step 1819.

As can be appreciated by one skilled in the art, a computer system withan associated computer-readable medium containing instructions forcontrolling the computer system can be utilized to implement theexemplary embodiments that are disclosed herein. The computer system mayinclude at least one computer such as a microprocessor, digital signalprocessor, and associated peripheral electronic circuitry.

1. A method for compressing received data comprising a first receivedcomponent and a second received component, the method comprising: (a)receiving an interleaved stream of a first plurality of first receivedcomponent samples with a second plurality of second received componentsamples; (b) converting the first received component to a firstconverted component in accordance with a statistical characteristic; and(c) converting the second received component to a second convertedcomponent in accordance with the statistical characteristic.
 2. Themethod of claim 1, further comprising: (d) determining the statisticalcharacteristic that is associated with the first received component andthe second received component.
 3. The method of claim 1, wherein (b)comprises mapping the first received component to a plurality of bins inaccordance with the statistical characteristic in order to form thefirst converted component and (c) comprises mapping the second receivedcomponent to the plurality of bins in accordance with the statisticalcharacteristic in order to form the second converted component.
 4. Themethod of claim 1, further comprising: (d) transforming the firstconverted component into a first transformed component and the secondconverted component into a second transformed component, wherein a firsttransformed plurality of first transformed component samples isinterleaved with a second transformed plurality of second transformedcomponent samples; (e) interleaving a first transformed plurality offirst transformed component samples with a second transformed pluralityof second transformed component samples; (f) quantizing the firsttransformed component into a first quantized transform and the secondtransformed component into a second quantized transform; and (g)interleaving a first quantized plurality of first quantized transformsamples with a second quantized plurality of second quantized transformsamples.
 5. The method of claim 4, further comprising: (h) encoding thefirst quantized transform into a first compressed component and thesecond quantized transform into a second compressed component; and (i)interleaving a first compressed plurality of first compressed componentsamples with a second compressed plurality of second compressedcomponent samples.
 6. The method of claim 1, wherein the first receivedcomponent corresponds to a received In-phase (I) component and thesecond received component corresponds to a received Quadrature (Q)component.
 7. A computer-readable medium having computer-executableinstructions that, when executed by a computer, cause the computer toperform steps recited in claim
 1. 8. A method for decompressing data inorder to approximate original data, the original data comprising a firstoriginal component and second original component, the method comprising:(a) receiving an interleaved stream of a first converted plurality offirst converted component samples and a second converted plurality ofsecond converted component samples; (b) obtaining a first convertedcomponent and a second converted component; and (c) converting the firstconverted component to a first decompressed component in accordance witha statistical characteristic.
 9. The method of claim 8, furthercomprising: (d) determining the statistical characteristic that isassociated with the first original component.
 10. The method of claim 8,wherein (c) comprises mapping the first converted component to the firstdecompressed component from a plurality of bins in accordance with thestatistical characteristic.
 11. The method of claim 8, wherein the firstoriginal component corresponds to an original In-phase (I) component andthe second original component corresponds to an original Quadrature (Q)component.
 12. The method of claim 8, wherein (a) comprises: (i)obtaining a first quantized transform and a second quantized transform;(ii) inverse quantizing the first quantized transform into a firsttransformed component and the second quantized transform into a secondtransformed component; and (iii) inverse transforming the firsttransformed component into the first converted component and the secondtransformed component into the second converted component.
 13. Themethod of claim 12, wherein (i) comprises: (1) obtaining a firstcompressed component and a second compressed component; and (2) decodingthe first compressed component into the first quantized transform andthe second compressed component into the second quantized transform. 14.A computer-readable medium having computer-executable instructions that,when executed by a computer, cause the computer to perform steps recitedin claim
 8. 15. An apparatus for decompressing data, the apparatuscomprising: a decompression module that obtains a first compressedcomponent and a second compressed component, that converts the firstcompressed component to a first decompressed component in accordancewith a statistical characteristic of the data, and that interleaves afirst plurality of first compressed component samples with a secondplurality of second compressed component samples.